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I've been looking for R packages that allow one to fit a logistic mixed model with an AR-1 correlation structure. I've found that it seems easy to do a logistic mixed model or to fit a linear mixed model with such a structure, but not both. Am I wrong in this, and if I'm not; what would an alternative be?

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The following simulates and fits a model where the linear predictor in the logistic regression follows a zero-mean AR(1) process, see the glmmTMB package vignette for more details.

> library(glmmTMB)
> set.seed(1)
> n <- 1000
> size <- 10
> eta <- arima.sim(list(ar=.8),n)
> y <- rbinom(n, prob=plogis(eta), size=size)
> group <- factor(rep(1,n))
> time <- factor(1:n)
> fit <- glmmTMB(cbind(y,size-y) ~ -1 + ar1(time + 0|group), family=binomial)
> summary(fit)
 Family: binomial  ( logit )
Formula:          cbind(y, size - y) ~ -1 + ar1(time + 0 | group)

     AIC      BIC   logLik deviance df.resid 
  4329.5   4339.3  -2162.8   4325.5      998 

Random effects:

Conditional model:
 Groups Name  Variance Std.Dev. Corr      
 group  time1 2.528    1.59     0.77 (ar1)
Number of obs: 1000, groups:  group, 1
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